Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "179" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 39 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 37 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459854 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 17.215061 | 17.498135 | 18.861967 | 20.453264 | 3.677991 | 4.257858 | 2.789576 | 3.549918 | 0.0369 | 0.0927 | 0.0412 | 1.204005 | 1.212359 |
| 2459853 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 14.261613 | 16.935125 | 25.612936 | 28.307057 | 7.383490 | 11.436296 | 2.040684 | 2.683283 | 0.0388 | 0.0578 | 0.0125 | 1.220271 | 1.220026 |
| 2459852 | digital_ok | 100.00% | 100.00% | 99.46% | 0.00% | 100.00% | 0.00% | 14.401679 | 18.708947 | 26.577140 | 29.699293 | 15.896520 | 20.186459 | 16.620306 | 16.185634 | 0.0348 | 0.1281 | 0.0667 | 1.212550 | 1.236732 |
| 2459851 | digital_ok | 100.00% | 100.00% | 88.61% | 0.00% | 100.00% | 0.00% | 11.195010 | 22.076135 | 27.144598 | 31.606907 | 21.632919 | 43.502241 | 12.068863 | 19.916689 | 0.0355 | 0.1407 | 0.0593 | 0.892451 | 0.907900 |
| 2459850 | digital_ok | 100.00% | 0.00% | 0.00% | 1.75% | 100.00% | 0.00% | -0.323427 | -0.848098 | -0.715674 | -0.442977 | 4.112232 | -0.518741 | 11.177892 | 1.475261 | 0.7347 | 0.7493 | 0.3701 | 2.845469 | 2.718975 |
| 2459849 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.928855 | -0.831521 | -0.549419 | -0.810875 | 3.634052 | -1.353656 | 7.867405 | -0.976494 | 0.7321 | 0.7422 | 0.3736 | 3.259957 | 3.091728 |
| 2459848 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.290974 | -0.551668 | -0.425889 | -1.079705 | 7.620157 | -1.087640 | 4.721847 | -0.888460 | 0.7108 | 0.7448 | 0.3956 | 2.673357 | 2.526260 |
| 2459847 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.154031 | -0.462135 | -0.143904 | -1.010063 | 4.430675 | -0.756276 | 3.556510 | -0.596169 | 0.7187 | 0.6815 | 0.4468 | 3.160185 | 2.989721 |
| 2459845 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.849836 | -0.500071 | -0.387599 | 0.131923 | 4.502215 | -1.573581 | 6.491174 | -1.288290 | 0.7077 | 0.7301 | 0.4093 | 4.635185 | 5.101200 |
| 2459844 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 1.698743 | 2.806105 | -0.055747 | 3.158749 | 14.882429 | 0.717759 | 3.015217 | 1.576994 | 0.0285 | 0.0265 | 0.0012 | nan | nan |
| 2459843 | digital_ok | 100.00% | 1.20% | 0.66% | 0.00% | 100.00% | 0.00% | 0.961015 | -0.434916 | -1.170971 | -1.207273 | 2.555120 | -0.992306 | 6.734754 | -0.519134 | 0.7251 | 0.7313 | 0.4162 | 4.407438 | 4.167485 |
| 2459842 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.248377 | -0.145069 | 0.214014 | 0.386481 | 4.191336 | 0.783581 | 1.253594 | 0.042971 | 0.7465 | 0.6561 | 0.2792 | 8.421163 | 8.957811 |
| 2459841 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 2.414631 | 3.634610 | 0.193135 | 2.110196 | 11.291602 | 1.224829 | 2.154852 | 1.822014 | 0.0284 | 0.0261 | 0.0016 | nan | nan |
| 2459840 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 110.604897 | 312.120939 | 50.278546 | 206.291940 | 320.639267 | 6594.876550 | 697.918286 | 10379.717033 | 0.0450 | 0.0214 | 0.0065 | nan | nan |
| 2459839 | digital_ok | 100.00% | - | - | - | - | - | 30.451380 | 41.386308 | 140.397816 | 196.027994 | 183.381509 | 413.744793 | 1268.352650 | 3081.952083 | nan | nan | nan | nan | nan |
| 2459838 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.008735 | 0.286624 | -0.898944 | 0.141224 | 7.428225 | -1.639014 | 3.699609 | -0.753475 | 0.6809 | 0.6452 | 0.4041 | 0.000000 | 0.000000 |
| 2459836 | digital_ok | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0379 | 0.0345 | 0.0023 | nan | nan |
| 2459835 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | -1.063875 | -0.760086 | -0.287293 | -0.273706 | 12.557950 | -1.272740 | 1.296011 | -0.913938 | 0.0358 | 0.0352 | 0.0028 | nan | nan |
| 2459833 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | -0.483481 | 0.244014 | 0.443627 | 1.327881 | 12.148813 | -0.389710 | 2.067507 | 1.449405 | 0.0367 | 0.0327 | 0.0019 | nan | nan |
| 2459832 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.195758 | 0.133129 | -1.055984 | -0.127496 | 3.396897 | -1.190882 | 7.094316 | -0.550181 | 0.7409 | 0.4696 | 0.5550 | 3.318981 | 3.035978 |
| 2459831 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | -0.085597 | 0.732906 | 1.789859 | 6.045606 | 1.610739 | 0.936051 | 1.367570 | 2.287349 | 0.0391 | 0.0322 | 0.0038 | nan | nan |
| 2459830 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.121219 | 1.152893 | -1.055763 | 0.021338 | 6.891660 | -1.530655 | 7.063952 | -1.058322 | 0.7450 | 0.4867 | 0.5402 | 4.885598 | 5.100486 |
| 2459829 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.714255 | 2.195250 | -0.417459 | 0.088382 | 3.693709 | -1.706710 | 10.711215 | -0.868631 | 0.6973 | 0.6126 | 0.4078 | 8.797915 | 5.291812 |
| 2459828 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.485859 | 1.062711 | -0.527006 | 0.123087 | 4.961027 | -0.701746 | 5.909206 | -0.669232 | 0.7384 | 0.4894 | 0.5210 | 0.000000 | 0.000000 |
| 2459827 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.104599 | 1.163918 | -0.792024 | 0.067664 | 2.194970 | -0.781281 | 0.681099 | -1.478662 | 0.0640 | 0.0643 | 0.0120 | -0.000000 | -0.000000 |
| 2459826 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.421057 | 0.313596 | -0.570132 | -0.080555 | 8.117856 | -1.329655 | 14.226338 | -0.358397 | 0.0711 | 0.0831 | 0.0178 | 0.000000 | 0.000000 |
| 2459825 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.085908 | -0.299432 | -0.512978 | 0.181104 | 3.644161 | -0.750996 | 0.201771 | -0.809213 | 0.0734 | 0.0744 | 0.0181 | 1.190762 | 1.193227 |
| 2459824 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 0.485771 | 0.864506 | -0.555155 | 0.348550 | 5.114376 | -1.132097 | 8.157282 | -0.598949 | 0.0684 | 0.0692 | 0.0117 | 0.955371 | 0.952720 |
| 2459823 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | 2.002835 | -0.135416 | -0.340247 | -0.024544 | 0.750044 | -0.033247 | 9.849564 | 0.163716 | 0.0651 | 0.0707 | 0.0171 | 0.877384 | 0.882741 |
| 2459822 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.344974 | 0.823320 | -0.940155 | 0.121739 | 4.012698 | -0.962325 | 4.379445 | -0.744692 | 0.0653 | 0.0702 | 0.0129 | 1.186536 | 1.189353 |
| 2459821 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.712995 | -0.565067 | -0.934483 | -0.270213 | 2.070449 | -0.596374 | 1.609851 | -0.615560 | 0.0520 | 0.0623 | 0.0121 | 1.236261 | 1.235982 |
| 2459820 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.138878 | 0.929840 | -0.733980 | -0.139017 | 5.522436 | 0.119448 | 6.961818 | -0.044019 | 0.0640 | 0.0655 | 0.0112 | 1.213106 | 1.211349 |
| 2459817 | digital_ok | 0.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | -0.793452 | 0.337476 | -0.930570 | -0.205615 | 0.433579 | -0.792208 | 0.299115 | -0.806917 | 0.0722 | 0.0847 | 0.0150 | 1.207773 | 1.211174 |
| 2459816 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 0.829031 | 0.639867 | -0.669125 | 0.375458 | 4.316428 | 0.418771 | 9.245096 | -0.186388 | 0.8389 | 0.5835 | 0.6056 | 3.693647 | 3.710528 |
| 2459815 | digital_ok | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | -0.385011 | 0.014002 | -0.761165 | -0.044224 | 4.543640 | -0.069163 | 6.629410 | -0.303116 | 0.7896 | 0.6523 | 0.5300 | 3.157788 | 3.126359 |
| 2459814 | digital_ok | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | digital_ok | 100.00% | 100.00% | 100.00% | 0.00% | 100.00% | 0.00% | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | 0.000000 | 0.000000 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | nn Power | 20.453264 | 17.498135 | 17.215061 | 20.453264 | 18.861967 | 4.257858 | 3.677991 | 3.549918 | 2.789576 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | nn Power | 28.307057 | 16.935125 | 14.261613 | 28.307057 | 25.612936 | 11.436296 | 7.383490 | 2.683283 | 2.040684 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | nn Power | 29.699293 | 14.401679 | 18.708947 | 26.577140 | 29.699293 | 15.896520 | 20.186459 | 16.620306 | 16.185634 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | nn Temporal Variability | 43.502241 | 11.195010 | 22.076135 | 27.144598 | 31.606907 | 21.632919 | 43.502241 | 12.068863 | 19.916689 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Discontinuties | 11.177892 | -0.323427 | -0.848098 | -0.715674 | -0.442977 | 4.112232 | -0.518741 | 11.177892 | 1.475261 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Discontinuties | 7.867405 | -0.928855 | -0.831521 | -0.549419 | -0.810875 | 3.634052 | -1.353656 | 7.867405 | -0.976494 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Variability | 7.620157 | -0.551668 | -0.290974 | -1.079705 | -0.425889 | -1.087640 | 7.620157 | -0.888460 | 4.721847 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Variability | 4.430675 | -0.462135 | -0.154031 | -1.010063 | -0.143904 | -0.756276 | 4.430675 | -0.596169 | 3.556510 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Discontinuties | 6.491174 | -0.500071 | 0.849836 | 0.131923 | -0.387599 | -1.573581 | 4.502215 | -1.288290 | 6.491174 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Variability | 14.882429 | 1.698743 | 2.806105 | -0.055747 | 3.158749 | 14.882429 | 0.717759 | 3.015217 | 1.576994 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Discontinuties | 6.734754 | -0.434916 | 0.961015 | -1.207273 | -1.170971 | -0.992306 | 2.555120 | -0.519134 | 6.734754 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Variability | 4.191336 | -0.248377 | -0.145069 | 0.214014 | 0.386481 | 4.191336 | 0.783581 | 1.253594 | 0.042971 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Variability | 11.291602 | 2.414631 | 3.634610 | 0.193135 | 2.110196 | 11.291602 | 1.224829 | 2.154852 | 1.822014 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | nn Temporal Discontinuties | 10379.717033 | 110.604897 | 312.120939 | 50.278546 | 206.291940 | 320.639267 | 6594.876550 | 697.918286 | 10379.717033 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | nn Temporal Discontinuties | 3081.952083 | 41.386308 | 30.451380 | 196.027994 | 140.397816 | 413.744793 | 183.381509 | 3081.952083 | 1268.352650 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Variability | 7.428225 | 0.286624 | -0.008735 | 0.141224 | -0.898944 | -1.639014 | 7.428225 | -0.753475 | 3.699609 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Variability | 12.557950 | -0.760086 | -1.063875 | -0.273706 | -0.287293 | -1.272740 | 12.557950 | -0.913938 | 1.296011 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Variability | 12.148813 | 0.244014 | -0.483481 | 1.327881 | 0.443627 | -0.389710 | 12.148813 | 1.449405 | 2.067507 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Discontinuties | 7.094316 | -0.195758 | 0.133129 | -1.055984 | -0.127496 | 3.396897 | -1.190882 | 7.094316 | -0.550181 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | nn Power | 6.045606 | -0.085597 | 0.732906 | 1.789859 | 6.045606 | 1.610739 | 0.936051 | 1.367570 | 2.287349 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Discontinuties | 7.063952 | -0.121219 | 1.152893 | -1.055763 | 0.021338 | 6.891660 | -1.530655 | 7.063952 | -1.058322 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Discontinuties | 10.711215 | 2.195250 | -0.714255 | 0.088382 | -0.417459 | -1.706710 | 3.693709 | -0.868631 | 10.711215 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Discontinuties | 5.909206 | 1.062711 | -0.485859 | 0.123087 | -0.527006 | -0.701746 | 4.961027 | -0.669232 | 5.909206 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Variability | 2.194970 | -0.104599 | 1.163918 | -0.792024 | 0.067664 | 2.194970 | -0.781281 | 0.681099 | -1.478662 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Discontinuties | 14.226338 | 0.313596 | 0.421057 | -0.080555 | -0.570132 | -1.329655 | 8.117856 | -0.358397 | 14.226338 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Variability | 3.644161 | -0.299432 | -0.085908 | 0.181104 | -0.512978 | -0.750996 | 3.644161 | -0.809213 | 0.201771 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Discontinuties | 8.157282 | 0.485771 | 0.864506 | -0.555155 | 0.348550 | 5.114376 | -1.132097 | 8.157282 | -0.598949 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Discontinuties | 9.849564 | -0.135416 | 2.002835 | -0.024544 | -0.340247 | -0.033247 | 0.750044 | 0.163716 | 9.849564 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Discontinuties | 4.379445 | -0.344974 | 0.823320 | -0.940155 | 0.121739 | 4.012698 | -0.962325 | 4.379445 | -0.744692 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Variability | 2.070449 | -0.565067 | -0.712995 | -0.270213 | -0.934483 | -0.596374 | 2.070449 | -0.615560 | 1.609851 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Discontinuties | 6.961818 | -0.138878 | 0.929840 | -0.733980 | -0.139017 | 5.522436 | 0.119448 | 6.961818 | -0.044019 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Variability | 0.433579 | -0.793452 | 0.337476 | -0.930570 | -0.205615 | 0.433579 | -0.792208 | 0.299115 | -0.806917 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Discontinuties | 9.245096 | 0.639867 | 0.829031 | 0.375458 | -0.669125 | 0.418771 | 4.316428 | -0.186388 | 9.245096 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | ee Temporal Discontinuties | 6.629410 | 0.014002 | -0.385011 | -0.044224 | -0.761165 | -0.069163 | 4.543640 | -0.303116 | 6.629410 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 179 | N12 | digital_ok | nn Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |